With the growing use of interventional imaging, concerns about increased exposure of the operators to ionizing radiation have become more prominent. Concern about increasing exposure to radiation is one of the biggest challenges facing radiology towards the future. Another major source of concern is studies which show a very wide degree of variation in the levels of exposure for the same procedure across different centers.
Currently, operator exposure to radiation exposure is handled by having operators wear a mobile dosimeter, such as the RaySafe i2 system. This system has been incredibly useful in increasing awareness of the radiation exposure. However, the radiation exposure is only measured at a single location. It is well known that the exposure can vary greatly across the operators' skin surface based on the C-arm angulation, the scan parameters, whether or not any shielding or protective devices are used, etc. For instance, studies indicate that the left and middle parts of the cranium received significantly higher doses than the right part for interventional cardiologists. The information provided by dosimeters, while valuable, cannot handle this heterogeneous nature of radiation distribution.
Monte-Carlo methods are considered the most accurate method of estimating the transport of radiation. Monte Carlo methods use discrete simulation of millions of individual particles (primarily photons and electrons), using stochastic models for simulating the transport of radiation along with different scattering modes like Rayleigh scattering and Compton scattering. In regions with tissue, the simulation can provide detailed information about the deposited radiation energy as the particles move through the tissue. Historically, these methods have been computationally expensive, requiring powerful supercomputers for generating results. With modern developments in computer architecture and graphical processing units (GPUs), powerful simulations can now be carried out in a few minutes on standard computing workstations.
For applications such as radiation therapy planning, where there is no urgent clinical need for real-time dose computation, such GPU based Monte Carlo simulation engines are sufficient. However for real-time applications that typically arise during image acquisition, such as planning an X-ray based image acquisition (e.g., Computed Tomography, C-arm CT etc.), dose calculation that take multiple minutes are not practical. In a dynamic operating environment, there is a need to update (in real-time) the dose map in an imaging room to alert the operators and provide real-time feedback on the dose received by the patients and/or by the operator.